A highly efficient photovoltaic (PV) system requires a maximum power point tracker to extract peak power from PV modules. The conventional variable step-size incremental conductance (INC) maximum power point tracking (MPPT) technique has two main drawbacks. First, it uses a pre-set scaling factor, which requires manual tuning under different irradiance levels. Second, it adapts the slope of the PV characteristics curve to vary the step-size, which means any small changes in PV module voltage will significantly increase the overall step-size. Subsequently, it deviates the operating point away from the actual reference. In this paper, a new modified variable step-size INC algorithm is proposed to address the aforementioned problems. The proposed algorithm consists of two parts, namely autonomous scaling factor and slope angle variation algorithm. The autonomous scaling factor continuously adjusts the step-size without using a pre-set constant to control the trade-off between convergence speed and tracking precision. The slope angle variation algorithm mitigates the impact of PV voltage change, especially during variable irradiance conditions to improve the MPPT efficiency. The theoretical investigations of the new technique are carried out while its practicability is confirmed by simulation and experimental results.

Software-defined networking (SDN) is a transformative approach for managing modern network architectures, particularly in Internet-of-Things (IoT) applications. However, ensuring the optimal SDN performance and security often needs a robust sensitivity analysis (SA). To complement existing SA methods, this study proposes a new SA framework that integrates design of experiments (DOE) and machine-learning (ML) techniques. Although existing SA methods have been shown to be effective and scalable, most of these methods have yet to hybridize anomaly detection and classification (ADC) and data augmentation into a single, unified framework. To fill this gap, a targeted application of well-established existing techniques is proposed. This is achieved by hybridizing these existing techniques to undertake a more robust SA of a typified SDN-reliant IoT network. The proposed hybrid framework combines Latin hypercube sampling (LHS)-based DOE and generative adversarial network (GAN)-driven data augmentation to improve SA and support ADC in SDN-reliant IoT networks. Hence, it is called DOE-GAN-SA. In DOE-GAN-SA, LHS is used to ensure uniform parameter sampling, while GAN is used to generate synthetic data to augment data derived from typified real-world SDN-reliant IoT network scenarios. DOE-GAN-SA also employs a classification and regression tree (CART) to validate the GAN-generated synthetic dataset. Through the proposed framework, ADC is implemented, and an artificial neural network (ANN)-driven SA on an SDN-reliant IoT network is carried out. The performance of the SDN-reliant IoT network is analyzed under two conditions: namely, a normal operating scenario and a distributed-denial-of-service (DDoS) flooding attack scenario, using throughput, jitter, and response time as performance metrics. To statistically validate the experimental findings, hypothesis tests are conducted to confirm the significance of all the inferences. The results demonstrate that integrating LHS and GAN significantly enhances SA, enabling the identification of critical SDN parameters affecting the modeled SDN-reliant IoT network performance. Additionally, ADC is also better supported, achieving higher DDoS flooding attack detection accuracy through the incorporation of synthetic network observations that emulate real-time traffic. Overall, this work highlights the potential of hybridizing LHS-based DOE, GAN-driven data augmentation, and ANN-assisted SA for robust network behavioral analysis and characterization in a new hybrid framework.

This study presents a smart grid architecture addressing challenges in renewable energy integration, including limited data use, fragmented analysis, and weak real-time control. The proposed multi-layer framework combines data input, multidimensional analysis, AI optimization, and output evaluation to improve performance. A MATLAB simulation models interactions among load demand, solar and wind generation, battery storage, and grid supply over 24 hours. Results show renewables contribute 60–75% of power, reducing reliance on fossil fuels. Battery storage balances supply and demand, while real-time monitoring, demand response, and automation enhance stability. Energy efficiency reaches about 92%, with lower transmission and distribution losses. Reliability indices (SAIDI 0.02–0.05 hours per customer and SAIFI 0.01–0.03 interruptions) indicate strong system resilience. Economic analysis shows savings of $60,000–$80,000 from reduced fuel use despite higher upfront costs. Environmental benefits include major carbon emission reductions. Socioeconomic impacts include job creation and improved energy access. Overall, integrating renewable energy with advanced smart grid technologies offers a sustainable, efficient, and economically viable solution that supports long-term energy resilience and climate objectives. This approach also enhances system flexibility by enabling adaptive control strategies under varying load and generation conditions, ensuring scalability and future integration of emerging energy technologies and digital grid innovations worldwide adoption.

Due to the continuous fluctuation of the prices of oil and the environmental impact on the economy, a renewable energy becomes debatable issue. The generation of electricity by harnessing the power of sea wave has been the most prominent research into renewable energy for over decades. Various researches have used various power take off mechanisms systems for converting wave power into electrical power such as the pelamis, oscillating water column, Archimedes wave swing etc. In this research, linear generator power take of method is used to convert the motion of wave at its interaction with the devices into electricity since this method offers a great power output at a minimum wave energy power than other methods. Feasibility studies to ascertain the exact location for mooring of the components were conducted using google search. The analysis of a hybrid quantitative and qualitative method is applied. In this aspect, statistical analysis of sea wave data and the consumption of electrical gadget of the faculty engineering of Regional Maritime University were analysed in order to design a suitable wave energy converter. Hydrodynamic analysis of wave energy converter was determined in order to establish the velocity of sea wave, for due to the incident wave, the energy from sea wave and the electrical power generated by the generator as a result of sea wave energy.

This paper investigates the transition of traditional electricity transmission systems into modern, low-carbon network essential for mitigating climate change and ensuring energy sustainability. The electricity sector remains a major contributor to global greenhouse gas emissions, making transmission modernization critical for large-scale integration of renewable energy sources such as solar, wind, and hydro. This study proposes a comprehensive carbon-aware control framework that integrates smart grid technologies, energy storage systems, and dynamic optimization models to enhance grid efficiency, reliability, and emissions performance. Using Ghana's power system as a case study, the research develops a MATLAB-based simulation of a 10-bus transmission network incorporating real-world generation data, load forecasting, and geographical analysis of renewable potential. Results indicate that integrating renewable energy with energy storage can reduce CO₂ emissions by up to 50%, from 238,000 kg to 119,000 kg, though economic viability remains challenging without policy support, subsidies, or carbon credits. The simulation also highlights the role of energy storage in smoothing intermittent generation and maintaining system stability. Financial analysis and load growth projections reinforce the need for scalable investment models and regulatory reforms to support long-term de-carbonization. The proposed framework bridges the gap between emissions metrics and grid operations, offering a robust tool for policy makers, utilities, and researchers. The findings demonstrate that a low-carbon grid is both technically feasible and environmentally necessary for a sustainable energy future.